cuda neural network

The article was transferred from the deep learning public numberDeep learning is a new field in machine learning that is motivated by the establishment and simulation of a neural network for analytical learning of the human brain, which mimics the mechanisms of the human brain to interpret data, examples, sounds and texts. Deep learning is a kind of unsupervised learning.The concept of deep learning derives

1. Recurrent neural Network (RNN) Although the expansion from the multilayer perceptron (MLP) to the cyclic Neural network (RNN) seems trivial, it has far-reaching implications for sequence learning. The use of cyclic neural networks (RNN) is used to process sequence data.

Open source Artificial Neural Network Computing Library FANN Learning Note 1These days machine learning is very fire, neural network is the machine learning algorithm is a more important one. This time I also took some effort, learned a little fur, by the way to do some study notes.There are many textbooks about the ba

first, the Origin Originally wanted to follow the traditional recursive algorithm to achieve maze game--> genetic algorithm to achieve maze game--> neural network maze game ideas, in this article also write how to use the neural network to achieve the maze, but the study, feel some trouble is not very good, so I chose

Article reproduced from: http://www.52analysis.com/R/1627.html Neural Network (optimization algorithm) Artificial neural Network (ANN), referred to as neural network, is a mathematical model or computational model that mimics th

Multi-Task confrontation learning [1] In order to gain robustness against noise, multi-task learning is introduced into three networks:-Input Network (green), used as feature extractor-Senone output Network (red), used as Senone classification-Domain output Network (blue), domain here refers to the type of noise, a total of 17 kinds of noise In order to increase

NIPS 2016 article: Intel China Research Institute on Neural Network compression algorithm of the latest achievementsHttp://www.leiphone.com/news/201609/OzDFhW8CX4YWt369.htmlIntel China Research Institute's latest achievement in the field of deep learning--"dynamic surgery" algorithm 2016-09-05 11:33 reproduced pink Bear 0 reviewsLei Feng Net press: This article is the latest research results of Intel China

I ask Xi Xi, a few days ago to play with a bit of MATLAB in the use of Neural network toolbox, and suddenly there is "palpable" the sense of the well-being. The other is nothing, but the data structure of the neural network is a bit "weird", if careless will cause the toolbox error. Here is the correct open posture for

Source: Michael Nielsen's "Neural Network and Deep leraning", click the end of "read the original" To view the original English.This section translator: Hit Scir master Xu Wei (https://github.com/memeda)Statement: We will be in every Monday, Thursday, Sunday regularly serialized the Chinese translation of the book, if you need to reprint please contact [email protected], without authorization shall not be r

4 activation function One of the things to be concerned about when building a neural network is what kind of activation function should be used in each separate layer. In logistic regression, the sigmoid function is always used as the activation function, and there are some better choices. The expression for the tanh function (hyperbolic Tangent function, hyperbolic tangent) is: The function image is: Th

Microsoft Research Asia chief researcher Sun JianHow accurate is the world's best computer vision system? On December 10 9 o'clock in the morning EST, the imagenet Computer Vision Recognition Challenge was announced--Microsoft Research Asia Vichier's researchers, with the latest breakthroughs in deep neural network technology, have won the title of all three major projects with absolute advantage in image c

Introduction to neural network programming (2): What are we writing during socket writing? Http://www.52im.net/thread-1732-1-1.html 1. IntroductionThis article is followed by the first article titled Neural Network Programming (I): Follow the animation to learn TCP three-way handshakes and four waves, and cont

regression model), the final result is reflected in the data is a straight line or a super plane, But if the data is not linear, the performance of these models will become worse. In view of this problem, there are many algorithms for classifying non-linear data, and neural network is one of the earliest. for a logistic regression model, it can be represented as shown:Where Xi is the individual component o

BP algorithm:　　1. is a supervised learning algorithm, often used to train multilayer perceptron.2. The excitation function required for each artificial neuron (i.e. node) must be micro-(Excitation function: the function relationship between the input and output of a single neuron is called the excitation function.) ）(If the excitation function is not used, each layer in the neural network is simply a linear

non-XOR (the same as 1, the difference is 0), all the output of our training model will be wrong, the model is not linear!2. Neural Network Introduction:We can construct the following models:(where a represents logic with, B is logical or inverse, C is logical OR)The above model is a simple neural network, we have con

1. Background:1.1 Inspired by neural networks in the human brain, there have been many different versions in history. 1.2 The most famous algorithms are the backpropagation of the 1980.2. Multilayer forward neural networks (multilayer feed-forward neural network)The 2.1 backpropagation is used on a multilayer forward

Cyclic neural network--Realization Gitbook Reading AddressKnowledge of reading address gradients disappearing and gradient explosions Network recall: In the circular neural network-Introduction, the circular neural